AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal.
Emergency care-sensitive conditions (ECSCs) require rapid identification and treatment and are responsible for over half of all deaths worldwide. Prehospital emergency care (PEC) can provide rapid treatment and access to definitive care for many ECSCs and can reduce mortality in several different se...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-12-01
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| Series: | PLOS Digital Health |
| Online Access: | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000406&type=printable |
| _version_ | 1797349354001924096 |
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| author | Dalton Breno Costa Felipe Coelho de Abreu Pinna Anjni Patel Joiner Brian Rice João Vítor Perez de Souza Júlia Loverde Gabella Luciano Andrade João Ricardo Nickenig Vissoci João Carlos Néto |
| author_facet | Dalton Breno Costa Felipe Coelho de Abreu Pinna Anjni Patel Joiner Brian Rice João Vítor Perez de Souza Júlia Loverde Gabella Luciano Andrade João Ricardo Nickenig Vissoci João Carlos Néto |
| author_sort | Dalton Breno Costa |
| collection | DOAJ |
| description | Emergency care-sensitive conditions (ECSCs) require rapid identification and treatment and are responsible for over half of all deaths worldwide. Prehospital emergency care (PEC) can provide rapid treatment and access to definitive care for many ECSCs and can reduce mortality in several different settings. The objective of this study is to propose a method for using artificial intelligence (AI) and machine learning (ML) to transcribe audio, extract, and classify unstructured emergency call data in the Serviço de Atendimento Móvel de Urgência (SAMU) system in southern Brazil. The study used all "1-9-2" calls received in 2019 by the SAMU Novo Norte Emergency Regulation Center (ERC) call center in Maringá, in the Brazilian state of Paraná. The calls were processed through a pipeline using machine learning algorithms, including Automatic Speech Recognition (ASR) models for transcription of audio calls in Portuguese, and a Natural Language Understanding (NLU) classification model. The pipeline was trained and validated using a dataset of labeled calls, which were manually classified by medical students using LabelStudio. The results showed that the AI model was able to accurately transcribe the audio with a Word Error Rate of 42.12% using Wav2Vec 2.0 for ASR transcription of audio calls in Portuguese. Additionally, the NLU classification model had an accuracy of 73.9% in classifying the calls into different categories in a validation subset. The study found that using AI to categorize emergency calls in low- and middle-income countries is largely unexplored, and the applicability of conventional open-source ML models trained on English language datasets is unclear for non-English speaking countries. The study concludes that AI can be used to transcribe audio and extract and classify unstructured emergency call data in an emergency system in southern Brazil as an initial step towards developing a decision-making support tool. |
| first_indexed | 2024-03-08T12:28:17Z |
| format | Article |
| id | doaj.art-5669eb8e4aa443cc9fa4141ca044ae34 |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-08T12:28:17Z |
| publishDate | 2023-12-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-5669eb8e4aa443cc9fa4141ca044ae342024-01-22T05:31:41ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702023-12-01212e000040610.1371/journal.pdig.0000406AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal.Dalton Breno CostaFelipe Coelho de Abreu PinnaAnjni Patel JoinerBrian RiceJoão Vítor Perez de SouzaJúlia Loverde GabellaLuciano AndradeJoão Ricardo Nickenig VissociJoão Carlos NétoEmergency care-sensitive conditions (ECSCs) require rapid identification and treatment and are responsible for over half of all deaths worldwide. Prehospital emergency care (PEC) can provide rapid treatment and access to definitive care for many ECSCs and can reduce mortality in several different settings. The objective of this study is to propose a method for using artificial intelligence (AI) and machine learning (ML) to transcribe audio, extract, and classify unstructured emergency call data in the Serviço de Atendimento Móvel de Urgência (SAMU) system in southern Brazil. The study used all "1-9-2" calls received in 2019 by the SAMU Novo Norte Emergency Regulation Center (ERC) call center in Maringá, in the Brazilian state of Paraná. The calls were processed through a pipeline using machine learning algorithms, including Automatic Speech Recognition (ASR) models for transcription of audio calls in Portuguese, and a Natural Language Understanding (NLU) classification model. The pipeline was trained and validated using a dataset of labeled calls, which were manually classified by medical students using LabelStudio. The results showed that the AI model was able to accurately transcribe the audio with a Word Error Rate of 42.12% using Wav2Vec 2.0 for ASR transcription of audio calls in Portuguese. Additionally, the NLU classification model had an accuracy of 73.9% in classifying the calls into different categories in a validation subset. The study found that using AI to categorize emergency calls in low- and middle-income countries is largely unexplored, and the applicability of conventional open-source ML models trained on English language datasets is unclear for non-English speaking countries. The study concludes that AI can be used to transcribe audio and extract and classify unstructured emergency call data in an emergency system in southern Brazil as an initial step towards developing a decision-making support tool.https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000406&type=printable |
| spellingShingle | Dalton Breno Costa Felipe Coelho de Abreu Pinna Anjni Patel Joiner Brian Rice João Vítor Perez de Souza Júlia Loverde Gabella Luciano Andrade João Ricardo Nickenig Vissoci João Carlos Néto AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal. PLOS Digital Health |
| title | AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal. |
| title_full | AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal. |
| title_fullStr | AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal. |
| title_full_unstemmed | AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal. |
| title_short | AI-based approach for transcribing and classifying unstructured emergency call data: A methodological proposal. |
| title_sort | ai based approach for transcribing and classifying unstructured emergency call data a methodological proposal |
| url | https://journals.plos.org/digitalhealth/article/file?id=10.1371/journal.pdig.0000406&type=printable |
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